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Generative AI will reshape every industry. But when?

There’s little question that gen AI has captivated business interest since ChatGPT launched at the end of 2022. Interest has only grown since that announcement and we believe it will transform organizations through new levels of human-machine collaboration. Throughout this guide you’ll find statistics, predictions and perspectives to spur thinking on how to pragmatically apply this technology to innovate.

The sense of urgency is understandable given rapid advancements. However, while most companies have actively explored gen AI’s potential through proofs of concept and early-stage experimentation this past year, Cognizant research shows that many leaders (30%) believe meaningful impact is still years away.

For most executives we engage, the question is not “if” but “how and when” gen AI will transform their business models and operations. Many are now looking to scale early successes through broader initiatives. Our own research and client conversations this past year reveal enthusiastic curiosity tempered by thoughtful diligence around these emerging capabilities. As enterprises look to transition experiments into scaled production-grade solutions, understandable caution accompanies the excitement.

Still, through skills-building and laying responsible foundations in 2023, companies equipped themselves for the next stage of maturity in leveraging AI’s generative potential. 2024 is the year to accelerate AI impact through focus and investment. The rules of engagement continue to rapidly evolve as practical experience refines our thinking on the possible. By working together, we can apply this technology practically and responsibly to increase productivity and deliver superior human-centric experiences.

Let’s shape the future together with care, creativity and purpose.

How we arrived here

An innovation boom 75 years in the making

In the summer of 2022—well before Open AI’s ChatGPT exploded into the public discourse—gen AI began generating media buzz when a recently-fired Google Engineer claimed1 their LaMDA model might be sentient.

True or not, this wasn’t an entirely surprising claim for artificial intelligence. We’ve been expecting this.

Since Alan Turing’s 1950 “Imitation Game” (Turing Test) proposal, we’ve imagined a future of computers with human-like intelligence, personality and autonomy. Today, we seem to be accelerating towards that disruptive future.

If and when we reach sentient AI is anybody’s guess but gen AI presents a significant new milestone in this journey, sparking new interest, innovation and discourse. Here’s how we arrived at this moment.

“Generative AI sits within the context of decades of research into AI. We are now entering the era where this technology will start to fundamentally transform businesses.”

Naveen Sharma

Head of Artificial Intelligence and Analytics Practice

1950s – 60s
blue arrow

The birth of AI

In the early days of modern computing, the Turing Test and the famed 1956 Dartmouth Workshop launch the era of AI. The first AI programs form and optimism is high.

1970s – Mid-90s
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The two AI winters

While progressive advances like expert systems proliferate and cultural interest in AI grows, underwhelming results lead to two periods of reduced funding and focus.

Mid-90s – 2000s
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The rise of machine learning

With the internet and accelerated business digitization, data availability and IT funding expand to drive practical AI applications. Major advances in robotics and data-driven solutions.

2010s - Present
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The Advent of Deep Learning

Significant breakthroughs in neural network and generative AI model development, accomplishing previously impossible tasks, alongside surge in big-tech investment. As of Q1 2024, the Crunchbase AI startup list has grown to nearly 10,000 companies2.

Where we’re headed

Change (and more change) ahead

In an April 2023 interview with CBS, Alphabet CEO Sundar Pichai famously commented that today’s generative AI will soon impact “every product across every company.”3 With rapid consumer adoption and the increased competitive pressure created by gen AI across industries, Pichai’s predication is sure to hold true.

As gen AI permeates markets, it’s critical that adaptability be built into the technology and cultural fabric of organizations. New, disruptive intra-industry and extra-industry use-cases will arise frequently in the coming years creating continuous change to navigate.

Hexagon abstract image

1

A much larger context window
Increasing context windows are critical for many enterprise use-cases and will allow for larger, more comprehensive prompts to be passed to models. This new access to vast contextual datasets will open even more doors for AI.

2

New gen AI models, expanded AI features in enterprise software
Next-gen models are already in development, including open-source models with more flexibility and control. Expect acceleration of new entrants and innovation. Enterprise platforms are adding AI tooling that will drive further proliferation.

3

Waves of regulation and standards
World governments will adopt and adapt regulations at lagging pace as they address rapidly evolving ethical, economic and societal concerns. Organizations will formalize AI governance roles with variable risk tolerance for use cases.

4

Generative video and AR/VR renaissance
With significant advancement in AR/VR technology spearheaded by Meta, Apple and Microsoft, compelling new applications backed by gen AI will launch. With conversational user interfaces (i.e., chat, voice), new visual worlds will be seen.

5

War for talent shifts to war for innovation
As 30% of work hours4 are expected to be directly impacted by AI and resulting automation capabilities, productivity gains will be felt by all. The war for technology talent will be reshaped as a war for technology innovation as organizations differentiate with data.

Meeting the AI natives

The forever digitally young

Operating effectively in the era of generative AI requires a reconstruction of the now decades-old digital maturity narrative. We’re entering a post-digital era where every enterprise is digital and what defines leaders is their adaptability—which extends to their definition of maturity, how they operate and what they sell.

The gen AI era is one of continuous, perpetual change. The fundamental definition of computation and chip design is changing with AI as rigid, linear and exact computations are swapped for the abstract and inexact logic that underpins neural network thinking. Companies must now operate in similar ways.

So, are there AI natives?

As noted in our gen AI timeline, there has been an explosion of AI-centric startups born over the past two years—these might be defined as AI natives. These companies focus on AI and, presumably, they have AI built into their operations and culture as well as their product.

But just as our definition of digital maturity requires a ‘continuous change’ perspective, so too will our definition of the “AI-native company”.

Being “born into” the gen AI era is far less important than exploration and adoption. Those organizations who pioneer AI—and set the rules early to gain competitive market share from it—will establish what it means to be an AI native. Enterprise organizations, with their robust proprietary data to build upon, have the advantage.

Early pioneers have common traits:

  • Modern operations and architecture: Delivery and IT infrastructures that leverage data and accelerate change
  • Enabled AI culture: Definitive policies and governance with access and training for staff to benefit from AI
  • A focus on disruptive propositions: Already piloting AI experiences to hedge against new entrant threats
  • Engaging suppliers to evolve services: Addressing gen AI opportunities for mutual benefit
  • Simplifying not complexifying: See gen AI as a way to transform value creation vs. ”just new tech”

How are early pioneers applying generative AI?

  • Expert assistants for professional work
    Gen AI assistants being used in legal software to augment services and accelerate work

  • Media generation
    Entertainment and creative studios using gen AI to create animation sequences, social reels and more

  • Market insights
    Aggregating customer feedback (reviews, call transcripts, etc.) for gen AI-driven insights

  • Text to software product generators
    Gen AI using component libraries, design systems and code bases to build POC software “ondemand”

  • Interactive chat-bots
    Transforming the classic chat-bot, gen AI powering conversational agents to answer and explain

Known strengths of generative AI

The blurring boundary between human and machine work

Gen AI presents a fundamental change in our understanding of what practical, immediately-accessible AI can do. Chat-bots, candidate screening tools, summarizers and picture-makers might inspire us today, but soon AI will shape the core of modern business.

It isn’t sentient but it sure does behave in human ways – and that’s what’s so inspiring about this technology. Whether finishing a sentence, writing the code for a component, ideating on novel molecular structures or animating an entire new movie, this generation of AI composes complex patterns and data to create.

As organizations come to understand the strengths and potential use-cases of gen AI, they also begin to realize the fundamental requirements within their organization for fully leveraging this technology.

It all starts with data. Combine that data with business processes and logic and gen AI is transformative.

“Generative AI provides completely new capabilities to automate and augment knowledge work. It is going to turbo-charge tasks that require creativity and expertise, such as design, engineering and quality assurance”

Pramod Bijani

Head of Digital Experience and Digital Engineering Delivery

1

Complex process automation
Core business processes that, in the past, have not been open to automation due to complexity and variability can now be managed and reshaped by AI

2

Data augmentation and completion
Gen AI systems work with data to provide first-line analysis, classification, sanitization and more, free from human error and at scale

3

Predictive analysis
Gen AI is capable of analyzing complex, structured or unstructured data to identify patterns and trends to form actionable recommendations

4

Driving efficiency and supporting knowledge work
AI makes knowledge work more efficiently by accelerating and expanding on ideation, distilling data to find insights, rapidly drafting and more

5

Real time optimization
Gen AI is capable of monitoring processes and outputs to proactively identify opportunities for improvement, prescribe and even implement changes

6

Multimedia generation
Gen AI is capable of both consumption and creation of rich media across text, audio, video and images, unlocking powerful new possibilities

Known weak points of generative AI

To err is human. For AI, it’s by design.

The cyclical evolution of AI over the past 75 years has been marked by periods of waxing enthusiasm and waning pessimism. As new advances promised new opportunities, institutions and businesses have jumped in and invested heavily in the technology. When outcomes haven’t met expectations, though, the AI space has experienced disillusionment and stagnation.

By its nature, AI is unpredictable and this is only further highlighted by the gen AI boom and the human-like creativity of its outputs.

Like humans and on many tasks, gen AI is capable of working flexibly towards a goal or target output rapidly and creatively. Also, like human workers, context matters.

Whether its brand values, ethical considerations, situational knowledge, historical learning, consumer needs or anything else, human workers are expected to understand the context of their work—and this can impact the output of their efforts. With generative AI, contextual understanding is often difficult to achieve “out of the box,” especially with consumer tools like ChatGPT. This has been the source of much of gen AI’s criticism.

Its strengths shape its weaknesses

Generative AI wants to answer us. It’s built to respond to our prompts—no matter their complexity—and often provides answers that, in a sense, acknowledge this fact. ChatGPT allows us to refresh responses. Image generators like OpenAI’s DALL-E or the popular Midjourney both return multiple images to any single prompt. These tools understand that they may be wrong.

The creative muscle of gen AI can be striking, but it is definitely not magic. Gen AI’s capabilities are fundamentally based on reference data and training.

AI adoption creates new categories of risk that require focused assurance at the enterprise level. Organizations that engage in this transformative technology with this in mind will gain the most from the AI era.

Hallucinations

When AI produces unreliable and erroneous outputs, it erodes data-driven strategy, reduces customer trust and limits operational efficiency.

Data quality and AI safety

Because data shapes AI’s knowledge base, any inadequate data inputs will create bias and limit accuracy, fairness and decision-making.

The explainability problem

When AI provides a decision, there is inherent uncertainty in its certainty. The larger and more complex a model, the harder to ascertain.

“Overcoming gen AI’s limitations, we are achieving very strong results in hybrid systems where generative and evolutionary AI models are combined to play to their strengths. This will be a critical foundation for successful adoption.”

Babak Hodjat

AI CTO

Gen AI’s open questions

Waiting is not the answer

As enterprise decision-makers consider their paths forward in this new AI-enabled world, questions are being asked that are as complex and difficult to navigate as the technology itself. How do we know our AI isn’t hallucinating? What ethical boundaries does this system present? How can we rely on answers, if we can’t explain how our systems reached them?

These questions largely mirror the ‘weaknesses’ discussed earlier, and their answers will be shaped through trial and error, learning and time.

What’s certain is that readying the organization to navigate this AI-enabled world is critical for future business performance—exploring these questions is a key part of that readiness.

We’ll explore near-term steps to take to address these concerns within the readiness section later in this guide.

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How can we ensure ethical usage?

Significant concerns for misuse and harm, bias in poorly trained systems and other negative outcomes of use

Cognizant answer: AI creates entire new categories of risk that require focused testing, governance and assurance. LLMOps creates a framework for such responsible usage

What legal implications should concern us?

Concerns for copyright, IP infringement and regulatory issues when dealing with protected data, privacy

Cognizant answer: These concerns are ‘native’ to digital business. Processes and tooling can help navigate and safeguard here and every business needs a unique plan

How can we ensure predictable output?

Concern over hallucinations, poorly structured or insufficient direction in business-impacting scenarios

Cognizant answer: Generative AI can never be expected to provide 100% predictability in output. This is why training, tuning and continuous monitoring must be integrated

How will this impact our brand or public perception?

Concern over customer preference against gen AI in key touchpoints and dilution of brand experience

Cognizant answer: As with other AI questions, the key is to test and train gen AI systems well before usage and be selectively transparent with customers about use

7 proven use cases of gen AI impact

Even at this early stage, the opportunities for generative Al across the enterprise are countless. With the right foundations, the only limitation of gen AI solution-building may be a company’s imagination.

With so much opportunity and so many questions, it can be hard to know where to start. As you’ll find in our discussion of gen AI readiness later in this guide, what’s key is that organizations begin exploring this technology early to identify their own opportunity spaces, safeguard against disruption and begin building skills.

Fortunately, there’s no need to start this journey from scratch. With the following seven example use-cases of generative AI, we’ll highlight just how varied the opportunity can be. Every part of the value chain across every industry stands to be disrupted in unique, differentiating ways as organizations bring their unique data, processes and POV to the discussion. Let’s dig in.

1. Market and competitive intel

Turning data into human-readable, actionable and contextualized guidance is a major strength of gen AI. Generative AI systems can be used to industrialize data collection from a range of sources, including curated market research, real-time customer and competitive behavior, internet scraping and primary user research. Whether structured or unstructured, this data empowers systems to drive a range of automated analysis, summarization and recommendations.

Ingestion of market data
 

Summarize and classify
Collate captured data to identify relevance to defined topics of interest

Coverage adjustment
Identify new, relevant topics and add granularity to existing topics being tracked

React as persona
Adopt customer and stakeholder personas and react to content

Gaining insights from data
 

Synthesize multiple sources
Correlate, compare and combine related content on a topic(s) to distill insights

Ideate
Respond to insights to generate new concepts and hypotheses

Prepare primary research
Define audiences and create qualitative and quantitative research questions

Developing and validating hypotheses

Concept mock-up
Mock up concepts and stimulus for research testing and user validation

Transcription
Transcribe research data, extracting findings, confidence levels and aspects requiring follow-up research

Packaging and distributing
 

Report writing
Generate narrative and explanation around raw data and derived insights

Insight navigator
Provide a natural language discussion interface to interact with the corpus of research data

2. Software development

Microsoft Visual Studio Code, the wildly popular integrated development environment (IDE), has long-supported GitHub’s Copilot product (by some estimates automating 40%-60% of code writing5) and now also integrates ChatGPT directly into the developer interface. But the utility of generative AI during software development goes well beyond writing components. The entire software development process is set to see transformation as this technology impacts creativity, quality, productivity, compliance, utility and more.

Generate ideas

Crowd sourced ideation
Collate employee ideas, feedback and data to identify new opportunities

Navigate current state
Engage with AI to discuss enterprise structure, performance, code base, etc.

User testing
Use AI for scope testing sessions, to summarize findings or suggest improvements

Develop concepts 

Concept mock-up
Create rapid product visualizations to bring to life new ideas and support user testing

Requirements elaboration
Extrapolate concepts to epics, stories and acceptance criteria

Implementation planning
Prioritize scope items, sequence to sprint plans and assign to teams based on functions and skills

Implement software 

Code completion
Create code based on collaborative developer input and code base context

Quality assurance
Assess risks and failure modes, build test scenarios, test data and automations to validate

Change management
Identify impacted groups and assist those groups in addressing impacts

Assurance and release

Release readiness
Inspect code and project actions to determine if go-no-go criteria are met and the solution is ready to release

3. Production

Product research, production and quality control will see significant Generative AI impact in the coming years as organizations across industries seek to unlock transformative new efficiency and product innovation ahead of competition. This zone is highly controlled and data-intensive, making it a perfect early adoption area. The IP established through smartly leveraging Generative AI in this space will reshape industries and establish new leaders.

Research and development

PRD shaping
Shape product requirements based on current and expected future demands

Materials research
Assist in identifying and optimizing materials for a variety of requirements

AI-powered scenario modeling
Emulate real-world scenarios virtually at vast scale to optimize product design

Design and prototyping

Generative design
Create numerous variants based on requirements, accelerate creative work

Tradeoff-based prototyping
Rapid virtual prototypes based on adjustable goals and balance of trade-offs

Niche user optimization
Simulate specific user scenarios, suggest variants that service new groups

Design critique AI
Partner with humans to critically respond to concepts and help shape design

Production

Cost/sustainability optimization
Analyze design-to-production pipeline for cost savings and sustainability

Production quality assurance
Systems to inspect products, ID defects or inconsistencies accurately at scale

Supply chain optimization
Predict supply chain disruptions, optimize sourcing and optimize logistics

Go to market

External systems integration
Gen AI can be integrated with ERPs and CRMs for further insights

Continuous improvement feedback loop
Analyze product performance data and feedback to drive refinements

4. Marketing and sales

Generative AI improves planning, production efficiency and effectiveness throughout the marketing and sales journey. As the technology gains adoption, asset production cycles will see a marked acceleration with a range of potential new asset types and channel strategies becoming available. Further, self-service channels will become more personalized and impactful while sales staff will increase their productivity and knowledge to focus more time on driving successful customer engagements.

Manage channels

Content production
Create digital assets: images, alt text, copy, translations, scripts, campaign plans and more

Software development
Create software to support interactive campaign elements

User testing
Scope testing sessions, summarize findings and suggest improvements

Nurture leads

Lead capture
Conversational data capture at lead identification stage to provide rich lead information and immediate customer response

Lead nurturing
Create personalized comms, content and experiences for engagement and nurturing leads

Sales prioritization
Identify the best activities for sales teams to focus on and craft personalised action plans

Manage opportunities

Sales effectiveness
Train sales staff on latest propositions, messaging and tactics

Sales presentations
Generatively compose presentations that combine sales strategy, customer context and standard offerings

Negotiate and close

Customer RFP responses
Respond automatically using best practice answers tailored to context

Negotiation and closure
Assemble contracts and statements of work to match proposals, MSAs, standard terms, etc.

5. Expert advisors

Generative AI streamlines and accelerates the provisioning of expert advice to benefit end-users and businesses alike. In many scenarios, gen AI has the capacity to act in a self-service model to provide expert guidance directly to users. Where complexity is higher or in safety-critical environments, gen AI can facilitate many stages of the process without acting in a fully autonomous way. With AI-driven pre- and post-processing, experts can more effectively utilize their time and focus on the highest-value or most-critical scenarios.

Customer requests service

Qualification
Understand user issue and determine if expert consultation is needed and appropriate

Triage
Understand the urgency and importance of the request to prioritize service

Collect information

Information extraction
Process unstructured data sources to extract the required information

Initial data capture
Address questions in a conversational experience to gather necessary data

Collection management
Chase participants to provide missing information and correct errors

Evaluate and recommend

Anomaly extraction
Draw attention to interesting or outlier input data features

Recommendation
Apply standard policies and procedures to the input context to suggest outcomes

Case discussion
Allow experts to interactively discuss the case with the necessary background knowledge to take action

Action recommendations

Downstream fulfilment
Interpret expert conclusions and trigger appropriate follow-up activities

Quality assurance
Review expert conclusions and highlight any causes for concern (e.g., policy misalignment, bias)

6. Employee engagement

Companies that adopt generative AI at a cultural level, going beyond asset production and chat interactions to elevate all common touch-points for customers and employees alike, will see the biggest gains in the coming years. Employee engagement is an exciting space for gen AI with the potential to impact recruiting, onboarding, team-building, performance management, support and more. The efficiency gains here will empower innovation across the business as gen AI permeates the market.

Recruit

Candidate screening
Review applications, assess fit and extract concerns prior to interview

Interview assistance
Suggest role-specific questions, transcribe and summarize conversations

Close
Develop tailored offer strategy to close strategy using interview transcripts

Onboard

New hire buddy
Conversational onboarding assistant to guide employee journeys for first month

Staffing
Leverage enriched findings to match new hires to roles and opportunities

Community connection
Suggest connection to communities of interest, sites, etc.

Enable

Accelerate admin
Simplify standard activities like timesheets, vacation requests, expense reporting, training, etc.

Support AI
Assist with self-service support for IT, human resources, etc.

Educational AI
Provide interactive training on skills development topics and answer questions on company data

Develop

Performance coach
Review activities, outcomes and feedback to suggest specific learning and development activities

7. Customer experience

Whether they’re just browsing or already a loyal customer, the way that people engage with brands throughout the shopping and post-purchase experience is set to dramatically evolve with gen AI. With answers becoming more seamless and appetite for content noise decreasing, customers will expect personal, intuitive, adaptive touch-points that understand and serve their needs. It’s time to put more you into UX.

Shopping and product comparison

Intelligent product suggester
Synthesize wide user data to create a new level of personalized shopping experience

Virtual demos / try-ons
Generative AI visualizes product in various settings, including worn, for better product qualifying

Contextual UI
Adaptive user interfaces, by context: behavioral, social, temporal, emotional, personal

Conversational UI
Users use natural language (voice or text) to engage, filter, qualify, assist, etc. during buying experience

Purchase, returns
 

Dynamic personalized pricing
Potential for novel AI-driven pricing mechanisms based on various inputs to optimize sales

Predictive return systems
Based on prior buying behavior, post-purchase engagements, etc. predict and address concerns

Fraud detection AI
AI for analyzing customer behavior to identify and respond to fraud or suggest new systems that might further prevent fraud

Loyalty
 

Generative loyalty programs
Novel loyalty programs using gen AI systems that personalize incentives

Churn prevention AI
ID behavioral patterns of at-risk customers and design engagements to prevent churn

Generative remarketing
Systems to dynamically build cohort-based and personalized remarketing strategies

Repeat
 

Behavioral campaign shaping
Gen AI learns from loyal buyers to identify/surface trends and formulate full-funnel campaigns

Deploying gen AI in a responsible and effective way

With all of the compelling use-cases for gen AI and the immediate accessibility of public tools in the market today, it can be easy to get carried away in the AI hype. That same consumer availability of basic AI tooling can trivialize the complexity and downplay the policy, process, partnership and skill required to build tailored, production-grade solutions.

Enterprise AI and machine-learning (ML) applications require automation engineering, data management, feature engineering, resource management, QA, and testing teams, IT operations and more; all aligned with coherent, shared accountability in order to effectively deliver solutions. In the following pages, we will explore how LLMOps expands our view of DevOps and how an updated view of quality engineering can safeguard AI solutions with holistic automated testing.

It all starts with setting strong, enforceable principles for responsible AI development.

Guiding principles for responsible AI development

Tactical principles for developing gen AI solutions in efficient, safe and value-oriented ways

Be robust, be safe

AI systems should perform reliably and safely. By building and deploying AI in accordance with best practices where we robustly test before deployment then monitor and improve operations regularly, we can reduce the risk of harm or unintended outcomes.

Benefit people and communities

Build a more sustainable and inclusive world through AI innovation. AI outcomes must incorporate human benefit and environmental sustainability in order to deliver impact and value to shareholders, users, customers, employees and society at large.

Protect privacy and respect boundaries

AI systems must be secure, compliant and respectful of people. Affirmative consent and a human-centered, privacy-first approach ensures sensitive data is never used unethically. A variety of auditing systems and safeguards are key within gen AI systems.

Design for transparency

AI systems should be understandable. Build trust and drive understanding through silo-breaking collaboration and rich communication across users and stakeholders, allowing them to understand AI systems and system outputs within their own, personal context.

Promote inclusivity and minimize bias

Bias exists in our data, models and our world; responsible AI systems seek to ensure AI is fair, unbiased and representative end to end and full-context. AI systems should treat people fairly and AI should be produced and reviewed by diverse teams.

Drive accountability and enable participation

People should be accountable and in control of AI systems. Clear processes and incentives for engagement create a culture where every individual is empowered to protect people, minimize risk and discover spaces of humane value.

LLMOps brings speed, support and safeguards to solution development

The following two pages provide an introduction to LLMOps but remain too high-level to sufficiently detail the orchestration of people, tooling and processes required to operationalize these practices. To explore more, engage with Cognizant’s generative AI team.

Gen AI solutions can be surprisingly seamless. The ability to understand users, act on their needs and provide human-like creative responses is what makes gen AI such a compelling solution today. Behind the scenes, though, gen AI solution development adds layers of complexity to the work of digital teams that go well beyond API keys and prompts. Orchestration is critical.

Consider the following difficulties:

  • Gen AI systems are difficult to test and unpredictable by nature – they cannot be validated and deployed in a ”set and forget” fashion

  • Development requires many skills and teams must rely on each other – this takes coherent organization and alignment programs

  • Gen AI systems must be at once predictable but flexible – this takes training and continuous monitoring

  • Generative AI development requires too many skills and processes to manage with manpower alone

  • Generative AI often leads to scenarios where privacy, regulatory compliance and data leakage are concerns—systems must be secure

  • Manual review of issues, anomalies and experience is untenable and might remove any ROI or advantage of these systems—automation is needed

Due to these conditions, new ways of working are required. Building on the popular concept of DevOps, LLMOps provides a path forward.

LLMOps defined: LLMOps aligns data engineering, agent development, software engineering and IT Operations to enable continuous integration, continuous delivery and continuous model training with an emphasis on automation and monitoring at all steps.

As organizations seek to develop effective generative AI- enabled solutions for internal and external users, defining and enforcing their own LLMOps approach is imperative.

This often starts with defining the KPIs of gen AI solutions (aligned to responsible AI principles) and ensuring that processes, governance and tooling are in place—made possible by LLMOps—to monitor and influence those KPIs.

These LLMOps KPIs can include:

Cycle time: 
Duration from initiation to deployment of gen AI-based solutions

Deploy frequency:
How often updates or new gen AI solutions are pushed to production

Review time:
Time taken to review and validate solution (model) outputs and performance

Automated solution ratio:
The proportion of tasks handled autonomously by the system

Data and model drift:
Discrepancies in solution (model) outputs or incoming data from the training scenario

LLMOps and AIOps are often confused, but they describe entirely different disciplines: LLMOps standardizes machine learning model deployment while AIOps automates IT operations.

Transparency and automation at the heart of LLMOps

As organizations tiptoe into gen AI, linear solution development processes will be favorable for proof-of-concept development at speed. The belief is that model training is something done early within a process and that a trained model can be utilized endlessly. This can work for testing the waters but is not a sustainable solution.

Mature LLMOps processes are iterative in nature with observability and automation at their heart. As a continuous cycle, LLMOps allows data intake and learning to regularly impact the solution while automating as much as possible and keeping humans in the loop. This feedback loop is key to responsible AI development. By ensuring that model behavior, application performance, data protection and system changes are controlled through a technology-driven workflow, organizations can operate more effectively.

“We must build quality and control into AI solutions to manage their continuous evolution. Due to their broad capabilities and emergent behavior, management is needed across the entire lifecycle.”

Andreas Golze

Head of Quality Engineering & Assurance Practice

Immature LLMOps
Mature LLMOps

Quality assurance and testing systems in a gen AI world

The evolved role of quality assurance’s (QA) teams and tooling within the delivery process will be a critical focus area for organizations seeking to deploy LLMOps.

While QA automation has become an area of strength for many mature engineering organizations, traditional approaches are insufficient for generative AI. The scope of QA and test automation has changed, with new driving factors to consider for AI-based applications.

QA
Updated QA automation capabilities must cover:
  • Systemic data assurance: Supports operations and decisions across process
    e.g., Feature selection analysis, input data augmentation, data fitment analysis

  • Systemic model assurance: Automation for monitoring model behaviors
    e.g., Model fairness analyzer, model validator, model interpreter, adversarial robustness tools

  • Generative output assurance: Automation to monitor outputs of gen AI
    e.g., naturalness, toxicity, coherence, demo polarity, clarity, truthfulness, relevance, etc.

  • Low data scientist QA efforts: Services and tooling to reduce specialist effort
    e.g., low-code/no-code solutions, customizable observability UI, etc.

  • Deep insights: Automation of insights from AI solutions / workflows
    e.g., gen AI tooling to explain solutions, model outputs, live data, etc.

Provisioning initial access to enterprise-grade gen AI tools

one checked

In the wake of ChatGPT’s emergence, it’s safe to say that every enterprise was abuzz with cautious excitement about the potential of this new technology.

At the individual team-member level, workers around the world began testing generative AI for their own use-cases. A recent survey from The Conference Board found that 56% of workers are using gen AI on the job, with nearly one in 10 employing the technology on a daily basis. Yet just 26% of respondents said their organization has a policy related to the use of generative AI, with another 23% reporting such a policy was under development.6

Organizations have been relatively quick to respond to these risks. Amazon, notably, found7 indication of proprietary data in public model usage and responded with a ban.

This isn’t luddite behavior, it’s just good practice. At this early stage, it’s unclear exactly how customer data, proprietary business data and other protected data is either being exposed to the operators of public LLMs or used to train the models themselves. Couple this with the simpler considerations of Privacy Policy adherence, Terms of Service, regulatory considerations and more bans are surely on the horizon.

But still, the advantages…

As new products go, any amount of friction (cost, risk, etc.) can have a chilling effect on adoption. But generative AI isn’t simply a new product; it’s a transformative technology that can change the world in striking, progressive ways.

Early adopters will have the advantage in this new world. Beyond the obvious cultural and process execution benefits of gen AI, we expect a patent boom in the coming years as organizations invent novel uses of gen AI-based tools within their business.

Preparing the business for gen AI means getting serious about near-term, safe-guarded adoption with well-integrated monitors and control of usage. Gain advantage while minimizing risk and learn as you go.

Steps to take
  • Update policies and governance
    A blocker for many orgs today, set clear policies and governance for a generative AI world

  • Educate employees actively, communicate often
    Clear communication to associates around access, policy leadership, support and appropriate usage

  • Set governance over approved services
    Clarify approved gen AI solutions and establish ownership for access provisioning and support

  • Identify and supervise adoption
    Maintain observability over approved AI services, monitor who is using them and for what purposes

  • Learn and capture best-practices
    Source insights and best practices within an organizational context, communicate learning

Design a path to scale successful POCs

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We’re quite early in generative AI’s diffusion of innovation bell-curve. Early adopters are establishing and quantifying basic use cases—gaining earned media as a result—and most would-be digital leaders are watching with curiosity.

POCs image

In an August 2023 report by Bain and Company8, only 6% of surveyed health systems executives have a clear gen AI strategy in place, yet 75% believe that the technology can reshape their industries. The reasons are the same that we’ve already discussed: uncertainty, risk, lack of inside knowledge and indecision. This lull in early adoption is where the advantage lies.

Quietly building a boom

To ready themselves for the road ahead, it is imperative that organizations go beyond provisioning access to public tooling and begin developing their own inside use-cases to drive a business case, spark thinking and lay a foundation for future development. This can be done in phased, controlled and protective ways.

We suggest two complimentary approaches:

1. Establish and run pilot projects

Pilot projects build a ‘light house’ for future innovation and expansion. By establishing specific initial goals for a cross-functional pilot project team to pursue, organizations can create disruptive proofs of concept and establish an internal POV.

2. Enable and accelerate with AI innovation “labs”

We have supported multiple organizations on establishing their own innovation lab environments where governance, collaboration and technology enablement are high. These environments become particularly powerful when formed in collaboration with hyperscalers who might provide innovative organizations with access to advanced models, education and specialized tooling.

Outcomes in focus
  • Build a business case for expanded focus
    Give enterprise context to gen AI use-cases by showing viability and capability against revenue-driving focus areas, expand on success.

  • Gain edge, protection from disruption
    Leverage gen AI early for disruptive use-cases that drive business value, safeguard the business and accelerate future projects.

  • Develop early internal SMEs and insight
    Begin establishing subject matter expertise within core internal teams by capturing and sharing knowledge during practical application.

  • Build internal and external reputation
    Establish industry point of view on gen AI, attract business and talent attention and set a cultural foundation for an AI enterprise.

Identify opportunities to add AI to the development process

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As covered in our section on LLMOps, generative AI development implies systemic changes to the way that software is delivered and supported within organizations. Zooming in, the same goes for software product engineering processes.

Here are just some ways that gen AI might impact product development.

Idea generation
The ability of Generative AI applications to work with trained models while evolving those models (and the application’s outputs) with the consumption of real-time data can unlock compelling use-cases for product idea-generation. Rather than relying on surveys and user reviews for qualitative data, Generative AI agents might deliver new concepts frequently based on real-time analytics. Product managers can then link these ideas to business goals and set a path forward.

Product design
As multimodal models (capable of intaking and outputting images, text, audio, etc.) mature and see enterprise adoption, “clickable prototype” design will become less a job for designers and instead be handled by gen AI tools. Fed with design principles, systems and reference designs, these prototype design tools will produce unbiased prototypes best fitting the market data available. The job of designers will be to identify the most promising solutions and refine them.

Risk mitigation
A core responsibility in product management is to manage and mitigate risk. With its predictive analytics capabilities, AI tooling can help in identifying potential risks and roadblocks early on in the prototyping phase. Quality, market readiness and future success can all be gauged by having algorithms analyze historic data, user preferences and even real-time market trends.

Resource optimization
Sustainability is the challenge of this generation of business. Generative AI can support sustainability efforts by optimizing resources and material mix for minimized waste and environmental friendliness. It can take regulatory processes into account, report on data and even affect subsequent production processes for both software and physical goods.

POCs image

Modernize and significantly automate core business operations

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There is no doubt that gen AI will revolutionize how we work. Process automation has long been a popular use-case in our digital world and AI is going to open entire new opportunity spaces here. The debate around automation will continue to be more focused on how regulators will impose limitations on the technology instead of how much potential the technology affords us.

Like media streaming, ride-sharing and other tech-driven economic catalysts that regulators have tried to stay in front of, resistance is a bit futile. Technology will be squeezed for as much juice as it can offer in a free market.

In our opening section of this document covering the future of gen AI, we touched on a shift from a war for talent (commonly discussed in the 2010s and pandemic era) towards a war for innovation as all businesses use gen AI to gain efficiency. AI-enabled automation of core business processes will drive this shift. Savings must be reallocated to accelerating business value.

1. Target core functions for AI-driven augmentation

Explore areas for generative AI to support knowledge work and accelerate creativity

2. Assess and expand on existing process automation

Apply gen AI solutions to existing process automations to further streamline for efficiency

3. ID opportunities to pre-process data

Utilize gen AI to reduce or replace human data processing, gaining speed and accuracy

4. Deploy generative assurance

Apply gen AI to business processes to continuously and completely audit quality, regulatory compliance and more

By 2032, few jobs could go untouched by generative AI

Some impact:
Exposure scores of at least 5%

Greatly impacted:
Exposure scores of at least 25%

Little impact:

Exposure scores of 5% or lower

Most jobs will see some change from gen AI, and over half could be greatly impacted.

Set new expectations with your suppliers

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It’s every company’s job to evolve

The early big press-makers of generative AI have been the expected parties. Hyperscalers have introduced new or evolved platforms for building AI solutions within their ecosystems. Myriad ultra-specialized startups have announced compelling new solutions to old problems (e.g., Hyfe’s10 cough sound monitoring for illness diagnosis). And service providers, like us, are launching new accelerators and labs for gen AI development.

But generative AI is coming for every product, in every market. That goes well beyond the early adopters and it includes you, your competition and your suppliers.

As you seek to leverage gen AI to unlock new efficiency, differentiate experiences, maximize quality, find cost-savings and evolve the business model, don’t discount the role your suppliers will play in these improvements.

Whether a service provider, a manufacture or raw goods provider, a logistics service or any other company that plays a role in your operations, there is an advantage to engaging early in a dialogue about gen AI.

Every one of your suppliers will be at a different stage of this journey. As they navigate use-cases, seek to answer questions about risks and control and otherwise dive into gen AI, join them.

From supplier to partner

As the innovation potential of generative AI becomes clear to more organizations, the opportunity to create wholly new experiences, services and processes by partnering with suppliers on a joint journey will become compelling for many businesses.

Going well beyond the cost savings of a joint investment, with enriched data, access to more skills and beyond, these partnerships might benefit both parties in dramatic ways when executed well. Consider the role of each key supplier within your service or product delivery and move the discussion beyond what they can do with AI for you.

Why engage now?

You stand to gain from their improvements

Suppliers are critical to your bottom line. Ask how they plan to improve SLAs, decrease total cost of ownership, operate faster and otherwise drive more business value for you and other customers.

You will learn from one another

As all companies are learning, work with suppliers to understand their own findings, partnerships and interest areas. Formalize shared learning between strategic teams and capture insight.

Open the door to more data

As you engage with your suppliers, consider internal solution opportunities and how supplier data might improve model training and solution delivery. Bring predictability, new product ideas, streamlined operations and more to life.

Pages

Introduction

Introduction

Landscape

Landscape

Basics

Basics

Applications

Applications

Responsible AI

Responsible AI

Readiness

Readiness